融合近红外与毫米波传感，用混合模型和元森林实现非侵入式血糖精准预测。
Non-Invasive Glucose Prediction System Enhanced by Mixed Linear Models and Meta-Forests for Domain Generalization
- 结合近红外与毫米波数据，用混合线性模型分析个体差异对血糖的影响。
- 在未见个体上达到17.47 mg/dL的平均绝对误差，表现稳定。
- 适合糖尿病管理、可穿戴设备研发及跨人群泛化研究者参考。
本研究提出一种非侵入式血糖预测系统，融合近红外（NIR）光谱与毫米波（mm-wave）传感技术。采用混合线性模型（MixedLM）分析毫米波频率S_21参数与血糖水平的关系，在异质数据集中考虑个体间差异，并整合多个预测因子，较传统相关分析更全面。同时引入领域泛化模型Meta-forests，有效应对数据集中的领域差异，提升模型对个体差异的适应能力。结果表明，该系统在未见受试者上表现良好，平均绝对误差（MAE）为17.47 mg/dL，均方根误差（RMSE）为31.83 mg/dL，平均绝对百分比误差（MAPE）为10.88%，具备临床应用潜力。该工作推动了精准、个性化、非侵入式血糖监测系统的发展。
原文摘要 · Abstract (English)
In this study, we present a non-invasive glucose prediction system that integrates Near-Infrared (NIR) spectroscopy and millimeter-wave (mm-wave) sensing. We employ a Mixed Linear Model (MixedLM) to analyze the association between mm-wave frequency S_21 parameters and blood glucose levels within a heterogeneous dataset. The MixedLM method considers inter-subject variability and integrates multiple predictors, offering a more comprehensive analysis than traditional correlation analysis. Additionally, we incorporate a Domain Generalization (DG) model, Meta-forests, to effectively handle domain variance in the dataset, enhancing the model's adaptability to individual differences. Our results demonstrate promising accuracy in glucose prediction for unseen subjects, with a mean absolute error (MAE) of 17.47 mg/dL, a root mean square error (RMSE) of 31.83 mg/dL, and a mean absolute percentage error (MAPE) of 10.88%, highlighting its potential for clinical application. This study marks a significant step towards developing accurate, personalized, and non-invasive glucose monitoring systems, contributing to improved diabetes management.
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